引用本文:颜雪松,张峻华,胡成玉.面向分布式同构混合流水车间绿色调度的多目标优化方法[J].控制理论与应用,2025,42(11):2286~2295.[点击复制]
YAN Xue-song,ZHANG Jun-hua,HU Cheng-yu.Multi-objective optimization method for distributed homogeneous hybrid flow-shop green scheduling[J].Control Theory & Applications,2025,42(11):2286~2295.[点击复制]
面向分布式同构混合流水车间绿色调度的多目标优化方法
Multi-objective optimization method for distributed homogeneous hybrid flow-shop green scheduling
摘要点击 3743  全文点击 147  投稿时间：2024-08-19  修订日期：2025-11-11
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DOI编号  10.7641/CTA.2024.40443
  2025,42(11):2286-2295
中文关键词  分布式混合流水车间调度  双碳目标  多目标优化  NSGA-II
英文关键词  distributed hybrid flow-shop scheduling  dual carbon goals  multiobjective optimization  NSGA-II
基金项目  国家重点研发计划项目(2022YFB4501402),湖北省重点研发计划项目(2023BAB065),国家自然科学基金项目(62073300)资助.
作者单位E-mail
颜雪松 中国地质大学(武汉)计算机学院 yanxs@cug.edu.cn 
张峻华 中国地质大学(武汉)计算机学院  
胡成玉* 中国地质大学(武汉)计算机学院 huchengyu@cug.edu.cn 
中文摘要
      在双碳目标的背景下,制造业的发展既面临挑战又面临机遇,为响应国家政策,大力减少碳排放量,本文以 分布式混合流水车间绿色调度问题作为研究对象,对于具有相同加工能力的工厂车间,本文构建了一个分布式同构 混合流水车间绿色调度问题模型,结合实际工厂特点,给出加工期间碳排放量的计算公式.结合问题特性提出了改 进的NSGA-II算法,设计了算法的混合初始化策略、更新策略和降碳策略以提高算法的性能,在算法的实验验证中, 设计消融实验验证了所提策略的有效性,并与多种先进的多目标优化算法进行对比实验,验证了改进算法在求解该 问题上的有效性.
英文摘要
      Under the background of the dual carbon goals, the development of the manufacturing industry faces both challenges and opportunities. In response to national policies, vigorously reducing carbon emissions, this paper focuses on the green scheduling problem of distributed hybrid flow shops. For factory workshops with the same processing capabilities, this paper constructs a green scheduling problem model for distributed homogeneous hybrid flow-shops. Combining the characteristics of actual factories, a formula for calculating carbon emissions during processing is provided. An improved NSGA-IIalgorithmisproposed, including a hybrid initialization strategy, an update strategy, and a carbon reduction strategy to enhance the algorithm’s performance. In the experimental validation of the algorithm, ablation studies were designed to verify the effectiveness of the proposed strategies. Additionally, comparative experiments with various advanced multi objective optimization algorithms validate the effectiveness of the improved algorithm in solving this problem.